The provided code reads data from the World Bank’s indicator for internet users (% of population), available at World Bank Internet Users Data, and processes it for visualization using Plotly. After reading the data, fill missing values by forward-filling along the rows to replace NaN values, ensuring a smoother time series representation. The cleaned data is then reshaped into a long format using pd.melt() to prepare it for plotting with Plotly. Non-numeric values are filtered out to ensure data integrity. This structured dataset, named melted_data, is now ready for visualization, providing insights into internet usage trends across different countries over time.
Load Python libraries commonly used for data analysis, and visualization.
import pandas as pd
import plotly.graph_objects as go
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn
import scipy
import kaleido
from PIL import Image
import plotly.io as pio
import io
from itables import init_notebook_mode
import os
import plotly.express as pxinit_notebook_mode(all_interactive=True)
data = pd.read_csv("interdata.csv")
# front fill to replace NA (2016 -> 2017 -> 2018)
clean_data = data.fillna(method = "ffill", axis=1)
# structure data for plotly
melted_data = pd.melt(clean_data, id_vars=['Country Name', 'Country Code'], var_name='Year', value_name='Value')
# remove rows with non-numeric values
melted_data = melted_data[pd.to_numeric(melted_data['Value'], errors='coerce').notnull()]
display(melted_data)| Country Name | Country Code | Year | Value | |
|---|---|---|---|---|
| Loading... (need help?) |
The code below creates an animated choropleth map to visualize country-wise percentage of internet users globally over time. Animation controls allow users to play through the frames, visualizing changes over time. This interactive visualization offers insights into the evolution of internet usage across different countries globally, providing a dynamic representation of population trends.
melted_data['Value'] = pd.to_numeric(melted_data['Value'])
melted_data['Year'] = melted_data['Year'].astype(str)
melted_data['Country Code'] = melted_data['Country Code'].astype(str)
pio.renderers.default = "notebook"
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/johan/world.geo.json/master/countries.geo.json') as response:
countries = json.load(response)
fig = px.choropleth_mapbox(
melted_data,
geojson=countries,
locations="Country Code",
color_continuous_scale="Ice",
animation_frame="Year",
color= 'Value',
labels={'Value': 'Percentage'},
mapbox_style="carto-positron", # Use a dark map style
zoom=1, # Set the initial zoom level,
range_color = (0,100),
opacity=0.5,
hover_name="Country Name",
height= 600,
width= 800,
)
fig.update_layout(
title= "Internet Users Globally",
geo=dict(
showframe=False,
showcoastlines=False,
),
paper_bgcolor='black', # Set the overall background color to black
font=dict(color='white'), # Set font color to white
height=500, # Set the height of the plot interface
updatemenus=[dict(type='buttons',
showactive=False,
y=-0.15,
x= -0.1,
xanchor='left',
yanchor='bottom')
],
)
fig.update_layout(
font=dict(
color="white", # Set font color to white
size=15, # Set font size
),
)
fig.update_layout(
hoverlabel=dict(
bgcolor="#18191A",
font_size=12,
font_family="Arial",
)
)
fig['layout']['sliders'][0]['pad'] = dict(r=70, t=0.0,)
fig.show()This code snippet converts the above interactive plot into a GIF which offers a dynamic portrayal of global internet usage trends over time, emphasizing localized shifts in internet adoption rates.
maps = []
for year in melted_data['Year'].unique():
# Filter data for the current year
data_year = melted_data[melted_data['Year'] == year]
# Create a Choropleth map for the current year
choropleth_map = go.Choroplethmapbox(
geojson=countries,
locations = data_year['Country Code'],
z = data_year['Value'],
text=data_year['Country Name'],
colorscale = 'Ice',
zmin = 0,
zmax = 100,
colorbar_title = 'Value',
name=str(year),
marker=dict(opacity=0.5),
)
fig = go.Figure(choropleth_map)
fig.update_layout(mapbox_style="carto-positron",
margin=dict(l=0, r=0, t=30, b=0), # Set margin to 0 to remove padding
title=f"Internet users by year {year}",
paper_bgcolor='black', # Set the overall background color to black
font=dict(color='white'), # Set font color to white
height= 600,
width= 1000,
mapbox=dict(
center=dict(lat=30, lon=0), # Set the center of the map
zoom=1, # Set the initial zoom level
)
)
fig.update_layout(
font=dict(
color="white", # Set font color to white
size=15, # Set font size
)
)
# Append map to map list
maps.append(fig.to_image(format="png", height= 600, width= 1000))
images_pil = []
# Convert each PNG image byte to a PIL Image object
for image_bytes in maps:
image_pil = Image.open(io.BytesIO(image_bytes))
images_pil.append(image_pil)
# create gif
images_pil[0].save("maps_animation.gif",
save_all = True,
append_images = images_pil[1:],
duration = 120,
loop = 0,
disposal=2,
quality = 50)
This snippet processes country data from a CSV file, adjusts column names, merges it with additional data based on country names, then displays the combined dataset, effectively consolidating continent and country information.
country_info = pd.read_csv("countryinfo.csv")
country_info.rename(columns={'name': 'Country Name'}, inplace=True)
merged_df = pd.merge(melted_data, country_info[['Country Name', 'region']], on='Country Name', how='left').dropna()
display(merged_df)| Country Name | Country Code | Year | Value | region | |
|---|---|---|---|---|---|
| Loading... (need help?) |
This code generates bar plots, depicting the internet usage percentage data against country with color encoding for continents. The plot includes animation for yearly changes.
# Plotting using Plotly Express
fig = px.bar(merged_df, x='Country Name', y='Value', color='region',
title='Value by Country', template = 'plotly_dark',
hover_data=['Country Code'], animation_frame="Year")
fig.update_xaxes(showticklabels=False, title_text='Countries')
fig.update_traces(textfont_size=12, textangle=0, textposition="outside", cliponaxis=False)
# Customize layout
fig.update_layout(
title="Internet users by year",
yaxis_title="Percentage",
font=dict(size=14),
height = 600
)
fig.update_layout(updatemenus=[dict(type='buttons',
showactive=False,
y=-0.34,
x=-0.09,
xanchor='left',
yanchor='bottom')
])
# Show plot
fig.show()A GIF is created from this interactive plot, capturing each frame’s visualization to showcase the dynamic evolution of internet usage across countries over time.
bars = []
for year in merged_df['Year'].unique():
# Filter data for the current year
data_year = merged_df[merged_df['Year'] == year]
# Create bars for the current year
bar = px.bar(data_year, x='Country Name', y='Value', color='region',
title='Value by Country', template = 'plotly_dark',
hover_data=['Country Code'])
bar.update_yaxes(range=[0, 100])
bar.update_xaxes(showticklabels=False)
bar.update_xaxes(tickangle=40)
bar.update_layout(height= 400)
bar.update_xaxes(title_text='Countries')
bar.update_yaxes(title_text='Percentage')
# Append bar to bar list
bars.append(go.Figure(data=bar).update_layout(title=f"Internet users by year {year}", font=dict(size=14)
).to_image(format="png"))
# Set labels for x-axis and y-axis
images_pil = []
# Convert each PNG image byte to a PIL Image object
for image_bytes in bars:
image_pil = Image.open(io.BytesIO(image_bytes))
images_pil.append(image_pil)
# create gif
images_pil[0].save("bars_animation.gif",
save_all = True,
append_images = images_pil[1:],
duration = 100,
loop = 0) 